Frequentist standard errors are a measure of uncertainty of an estimator, and the basis for statistical inferences. Frequestist standard errors can also be derived for Bayes estimators. However, except in special cases, the computation of the standard error of Bayesian estimators requires bootstrapping, which in combination with Markov chain Monte Carlo can be highly time consuming. We discuss an alternative approach for computing frequentist standard errors of Bayesian estimators, including importance sampling. Through several numerical examples we show that our approach can be much more computationally efficient than the standard bootstrap.

中文翻译：

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贝叶斯估计器的常见标准误。

经常性标准误是估计量不确定性的量度，是统计推断的基础。也可以为贝叶斯估计量得出频率要求标准误差。但是，除特殊情况外，贝叶斯估计量的标准误的计算需要自举，这与Markov链蒙特卡洛相结合可能会非常耗时。我们讨论了一种计算贝叶斯估计量的频繁性标准误（包括重要性抽样）的替代方法。通过几个数值示例，我们证明了我们的方法比标准的引导程序具有更高的计算效率。